Version 1
: Received: 8 February 2023 / Approved: 10 February 2023 / Online: 10 February 2023 (08:06:55 CET)
How to cite:
Park, B.; You, W. CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction. Preprints2023, 2023020188. https://doi.org/10.20944/preprints202302.0188.v1
Park, B.; You, W. CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction. Preprints 2023, 2023020188. https://doi.org/10.20944/preprints202302.0188.v1
Park, B.; You, W. CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction. Preprints2023, 2023020188. https://doi.org/10.20944/preprints202302.0188.v1
APA Style
Park, B., & You, W. (2023). CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction. Preprints. https://doi.org/10.20944/preprints202302.0188.v1
Chicago/Turabian Style
Park, B. and Wonsang You. 2023 "CF-ESRT: Coarse-to-Fine Efficient Super Resolution Transformers for Clinical-to-Dermoscopic Image Reconstruction" Preprints. https://doi.org/10.20944/preprints202302.0188.v1
Abstract
Deep learning technologies for skin cancer detection have been dramatically advanced based on high resolution dermoscopic images. The low-cost approach based on clinical skin images of low resolution is promising but remains technically challenging due to their undermined image quality. In this paper, we propose a coarse-to-fine efficient super resolution transformer (CF-ESRT) network to reconstruct the dermoscopy-level high resolution skin image from a low resolution clinical image. By connecting the refinement network to the original super resolution transformers and applying perceptual and gradient losses, our framework noticeably improves the finer texture details of skin lesions in the super resolution (SR) images, and is effective to elevate the perceptual quality of the SR images. Quantitative and qualitative evaluations show that our method outperforms ESRT the basis model as well as the other state-of-the-art SR models.
Keywords
Super resolution; Transformer; Deep Learning; Skin image; Dermoscopy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
